1. Field of the Invention
The present invention relates to a computational efficient algorithm for tissue compression analysis for free-hand static elasticity imaging. More specifically, this invention relates to an elasticity imaging system that employs medical diagnostic ultrasound imaging equipment to produce strain images.
2. Description of Related Art
It has been proved that pathological conditions often produce changes in biological tissue stiffness. Tumor tissues, for example, are known to exhibit mechanical properties different from the surrounding tissue, as indicated by the use of palpation as a diagnostic tool. Breast and prostate tumors are especially susceptible to changes in mechanical properties, as indicated in an article by T. A. Krouskop, T. M. Wheeler, F. Kallel, B. S. Garra, and T. Hall, entitled “Elastic moduli of breast and prostate tissues under compression.”, Ultrasonic Imaging, 20:260–274, 1998, which is incorporated by reference herein.
Many cancers, such as scirrhous carcinoma of the breast, appear as extremely hard nodules. However, a lesion may or may not possess echogenic properties that would make it detectable with conventional diagnostic ultrasound imaging systems. Tumors of the prostate or the breast may thus be difficult to distinguish with conventional ultrasound techniques, yet may still be much stiffer than the surrounding tissue, as reported in an article by B. S. Garra, I Cespedes, J. Ophir, S. Spratt, R. A. Zuurbier, C. M. Magnant, and M. F. Pennanen, entitled “Elastography of breast lesions; initial clinical results,” Radiology, 202:79–86, 1997, which is incorporated by reference herein. As the echogenity and the stiffness of tissue are in general uncorrelated, Garra et al. observe it is expected that imaging the hardness of the biological tissue will provide new information related to the pathological conditions, facilitating the diagnosis process.
The experimentally obtained elastic modulus data in normal and abnormal breast tissues at different frequencies and precompression strain levels was reported in the aforementioned article “Elastic moduli of breast and prostate tissues under compression.” The data in the article shows that the differences between the elastic moduli of the various tissues of the breast may be useful in developing methods to distinguish between benign and malignant tumors. Tissues of the prostate were also examined as cancers of the prostate are also significantly stiffer than normal tissue. Similar data indicating differences between the elastic moduli for normal and abnormal prostate tissues were also reported.
The imaging modality that facilitates the display of mechanical properties of biological tissue is called elastography. The purpose of elastography is to display an image of the distribution of a physical parameter related to the mechanical properties of the tissue for clinical applications. In addition to the aforementioned breast and prostate applications of elastography, successful results have been reported for muscle and myocardial applications by F. Kallel, J. Ophir, K. Magee, and T. A. Krouskop, entitled “Elastographic imaging of low-contrast elastic modulus distributions in tissue.”, Ultrasound in Med. & Biol, (409–425), 1998; E. E. Konofagou, J. D'Hooge, and J. Ophir, entitled “Myocardial elastography—a feasible study in vivo.”, Ultrasound in Med. & Biol. 28(4):475–482, 2002, which is incorporated by reference herein.
Elasticity imaging consists of inducing an external or internal motion to the biological tissue and evaluating the response of the tissue using conventional diagnostic ultrasound imaging and correlation techniques. Depending on the imaging mode and on the nature of tissue motion, elasticity imaging applications are divided into three distinct categories: a) static elasticity (also known as strain-based, or reconstructive) that involves imaging internal motion of biological tissue under static deformation; b) dynamic elasticity (also known as wave-based) that involves imaging shear wave propagation through the tissue; and, c) mechanical elasticity (also known as stress-based and reconstructive) that involves measuring surface stress distribution of the tissue.
Each of the three elasticity imaging applications comprises three main functional components. First, the data are captured during externally or internally applied tissue motion or deformation. Second, the tissue response is evaluated, that is, displacement, strain, and stress are determined. Lastly, the elastic modulus of the tissue is reconstructed using the theory of elasticity. The last step involves implementing the theory of elasticity into modeling and solving the inverse problem from strain and boundary conditions to elastic modulus. As the boundary conditions and the modeling of theory of elasticity are highly dependent on the structure of the biological tissue, the implementation of the last step is rather cumbersome and typically not performed. Moreover, the evaluation and display of tissue strain in the second step is considered to deliver an accurate reproduction of the tissue's mechanical properties.
Static elasticity imaging application is the most frequently used modality. In this application, a small quasi-static compressive force is applied to the tissue using the ultrasound imaging transducer. The force can be applied either using motorized compression fixtures or using freehand scanning. The RF data before and after the compression are recorded to estimate the local axial and lateral motions using correlation methods. The estimated motions along the ultrasound propagation direction represent the axial displacement map of the tissue and are used to determine the axial strain map. The strain map is then displayed as a gray scale or color-coded image and is called an elastogram.
While the majority of the elasticity imaging work has been concentrated so far on off-line processing, proof of concept and method optimization, real-time oriented applications have been only recently reported by Y. Zhu and T. J. Hall, entitled “A modified block matching method for real-time freehand strain imaging.”, Ultrasonic Imaging, 24:161–176, 2002, which is incorporated by reference herein; and by T. Shiina, M. Yamakawa, N. Nitta, E. Ueno, T. Matsumura, S. Tamano, and T. Mitake, entitled “Clinical assessment of real-time, freehand elasticity imaging system based on the combined autocorrelation method.”, 2003 IEEE Ultrasonics Symposium, pages 664–667, which is incorporated by reference herein. The need for real-time elasticity imaging applications in clinical environment is primarily of a practical nature. However, real-time elasticity imaging is indeed needed to acquire and process the ultrasonic echo data in such a way that patient-scanning time is relatively low and diagnostically relevant elasticity images are produced immediately during the scan. Thus, such real-time elasticity imaging systems are capable of displaying ultrasonic B-mode images and strain images on the same screen in real-time. Such a display also facilitates the assessment of the clinical relevance of the strain images being obtained.
Furthermore, the real-time processing of the ultrasonic echo data allows for freehand compression and scanning of the biological tissue rather than utilizing bulky and slow motorized compression fixtures. Freehand compression, as opposed to motorized compression facilitates a more manageable and user-friendly scanning process and allows for a larger variety of scanning locations. Its disadvantage, however, consists of exhaustive operator training, as the sonographer constantly needs to adjust the compression technique to obtain strain images of good quality. In more detail, to obtain strain images of consistent dynamic range (“DR”) and signal-to-noise ratio (“SNR”), the sonographer needs to maintain a constant compression rate while avoiding lateral and out-of-plane tissue motions. Moreover, the compression has to be performed exclusively on the axial direction of the imaging transducer while maintaining a certain speed and repetition period.
In short, due to the extremely complex nature of the tissue compression, obtaining elasticity images of consistent quality using free-hand strain imaging is neither trivial nor as expeditious as obtaining good quality B-mode images, thus real-time compression feedback is necessary to ensure proper operator training.
In an attempt to overcome the limitations discussed above, a few research groups proposed and implemented real-time static elasticity imaging systems as reported by Y. Zhu and T. J. Hall, entitled “A modified block matching method for real-time freehand strain imaging.”, Ultrasonic Imaging, 24:161–176, 2002, which is incorporated by reference herein; and, by T. Shiina, M. Yamakawa, N. Nitta, E. Ueno, T. Matsumura, S. Tamano, and T. Mitake, entitled “Clinical assessment of real-time, freehand elasticity imaging system based on the combined autocorrelation method.”, 2003 IEEE Ultrasonics Symposium, pages 664–667, which is incorporated by reference herein. In addition, U.S. Pat. No. 6,508,768 B1 to Hall et al. (“'768 patent”) describes in detail a real-time static elasticity imaging procedure and implementation. However, those implementations disclosed by the '768 patent and the Zhu et al. and Shiina et al. articles do not account completely for all the limitations mentioned above.
More particularly, neither the articles by Zhu et al. and Shiina et al. nor the teachings of the '768 patent provide a quantitative indication of the compression quality being achieved by the operator. Moreover, the operator does not receive guidance in order to improve the compression quality when s/he is only provided strain images that may contain artifacts and poor SNR. One of several drawbacks being that possible artifacts present in the strain image cannot be qualitatively linked to poor compression quality. Additionally, the current implementations calculate and display strain images continuously, independently of the quality of the compression, or even in the absence of compression. Therefore the computational burden placed upon the imaging system is extremely high while only select sets of strain images faithfully indicate the mechanical properties of the imaged tissue and are artifact-free. Moreover, depending on the applied compression rate, strain images are displayed with variable (and less than optimal) DR and SNR, allowing for artifacts.
There exists a need for a computational efficient algorithm capable of providing real-time tissue compression quality and quantity feedback to the operator. There also exists a need for a computational efficient algorithm that automatically selects the most advantageous pre- and post-compression frame pairs for delivering elasticity images of optimal dynamic ranges and signal-to-noise ratios. There further exists a need for a computational efficient algorithm that generates compression quality feedback independently of the quality of the compression being achieved. There exists still yet a need for a computational efficient algorithm that measures, analyzes and visually displays both the axial and lateral displacements (negative and positive) of the decompression of tissue. There exists further still a need for a computational efficient algorithm that captures and archives all information utilized in generating the elasticity images for off-line analysis.